||The trends of mergers and acquisitions (M&A) have been globalized recently. To meet different strategical objectives, companies seek for mergers and acquisitions. According to relevant data suggested, there are numerous of M&A taken place every year, however, the failure rate is relatively high, 70%-90%. |
There are some possible factors, both internally and externally, leading M&A to success or failure. However, the key to increasing the possibility of being successful in M&A is to select the target company fitting the same strategic objectives. Different strategic of M&A should come up with the measurement of target respectively. According to the literature review, it turns out that most of the M&As were based on financial point of view to measure the target, leading to the limited outcome. Therefore, this article suggests a new point of view. Applying the horizontal merger as the example, calculate the culture fit between cooperation and centrality, two distinct indicators, through text mining, analyzing the annual report and related text data. Combining these textual indicators with financial indicators, to verify the impacts of target selection from overall points of view.
As the result of related prediction literature of M&A, most research only adopt one classification algorithm to predict the target. Thus, this research will implement the variety of methods to predict the targets of M&A. The methods will be Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF). By employing the above methods, this research will focus on the listed and OTC company of electronic industry in Taiwan during the year 2014 to 2016. Furthermore, through confusion matrix to compare the results from different classifiers, to assist decision makers to select the target of M&A.
The proved outcome verifies that there will be a certain level of influences from non-financial indicators on the target selection of M&A. The performance of predicting the outcome by combining both textual and financial indicators will be higher than adopting only financial indicators. However, the results stated that the impacts from financial indicators in the model of prediction still is significance. This is the gesture that non-financial indicators could play the supporting role. In future research, it could put more perspectives and types of M&A decision making into consideration. By doing so, it could build a complete prediction model to assist mergers in selecting their targets.